{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['False_',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__array_api_version__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_attributes__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__numpy_submodules__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_expired_attrs_2_0',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_msg',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_type_info',\n",
       " '_typing',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'add',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfortranarray',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'asmatrix',\n",
       " 'astype',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_count',\n",
       " 'bitwise_invert',\n",
       " 'bitwise_left_shift',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_right_shift',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'char',\n",
       " 'character',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concat',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'core',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'f2py',\n",
       " 'fabs',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isdtype',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issubdtype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'long',\n",
       " 'longdouble',\n",
       " 'longlong',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'matrix_transpose',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'number',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'permute_dims',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'pow',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctypeDict',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_printoptions',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'strings',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'transpose',\n",
       " 'trapezoid',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulong',\n",
       " 'ulonglong',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unique_all',\n",
       " 'unique_counts',\n",
       " 'unique_inverse',\n",
       " 'unique_values',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vecdot',\n",
       " 'vectorize',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('600036')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     600036.SH   20241225  39.23  39.56  38.99  39.40      39.22    0.18   \n",
      "1     600036.SH   20241224  38.35  39.29  38.32  39.22      38.35    0.87   \n",
      "2     600036.SH   20241223  37.99  38.70  37.98  38.35      37.91    0.44   \n",
      "3     600036.SH   20241220  38.05  38.28  37.86  37.91      38.02   -0.11   \n",
      "4     600036.SH   20241219  38.38  38.53  38.00  38.02      38.52   -0.50   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3613  600036.SH   20100108  16.88  16.98  16.68  16.91      16.91    0.00   \n",
      "3614  600036.SH   20100107  17.33  17.43  16.78  16.91      17.36   -0.45   \n",
      "3615  600036.SH   20100106  17.69  17.69  17.31  17.36      17.73   -0.37   \n",
      "3616  600036.SH   20100105  17.71  18.00  17.34  17.73      17.71    0.02   \n",
      "3617  600036.SH   20100104  17.93  18.11  17.66  17.71      18.05   -0.34   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      0.4589   755913.07  2972981.821  \n",
      "1      2.2686   993428.46  3875223.368  \n",
      "2      1.1606   902691.56  3473126.152  \n",
      "3     -0.2893   542150.38  2060744.582  \n",
      "4     -1.2980   611195.82  2336249.857  \n",
      "...       ...         ...          ...  \n",
      "3613   0.0000   803598.62  1351767.271  \n",
      "3614  -2.5900  1019431.96  1742282.280  \n",
      "3615  -2.0900   774277.06  1348702.948  \n",
      "3616   0.1100   914013.73  1615349.974  \n",
      "3617  -1.8800   746135.58  1332370.091  \n",
      "\n",
      "[3618 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('157e986ba612461d0ebd381317b90a705858c65b02abdd1d85eb1a01')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"600036.SH\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     600036.SH   20241225  39.23  39.56  38.99  39.40      39.22    0.18   \n",
      "1     600036.SH   20241224  38.35  39.29  38.32  39.22      38.35    0.87   \n",
      "2     600036.SH   20241223  37.99  38.70  37.98  38.35      37.91    0.44   \n",
      "3     600036.SH   20241220  38.05  38.28  37.86  37.91      38.02   -0.11   \n",
      "4     600036.SH   20241219  38.38  38.53  38.00  38.02      38.52   -0.50   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3613  600036.SH   20100108  16.88  16.98  16.68  16.91      16.91    0.00   \n",
      "3614  600036.SH   20100107  17.33  17.43  16.78  16.91      17.36   -0.45   \n",
      "3615  600036.SH   20100106  17.69  17.69  17.31  17.36      17.73   -0.37   \n",
      "3616  600036.SH   20100105  17.71  18.00  17.34  17.73      17.71    0.02   \n",
      "3617  600036.SH   20100104  17.93  18.11  17.66  17.71      18.05   -0.34   \n",
      "\n",
      "      pct_chg         vol       amount  \n",
      "0      0.4589   755913.07  2972981.821  \n",
      "1      2.2686   993428.46  3875223.368  \n",
      "2      1.1606   902691.56  3473126.152  \n",
      "3     -0.2893   542150.38  2060744.582  \n",
      "4     -1.2980   611195.82  2336249.857  \n",
      "...       ...         ...          ...  \n",
      "3613   0.0000   803598.62  1351767.271  \n",
      "3614  -2.5900  1019431.96  1742282.280  \n",
      "3615  -2.0900   774277.06  1348702.948  \n",
      "3616   0.1100   914013.73  1615349.974  \n",
      "3617  -1.8800   746135.58  1332370.091  \n",
      "\n",
      "[3618 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock600036.csv\")\n",
    "stockname='招商银行'\n",
    "stockcode='600036'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3608</th>\n",
       "      <th>3609</th>\n",
       "      <th>3610</th>\n",
       "      <th>3611</th>\n",
       "      <th>3612</th>\n",
       "      <th>3613</th>\n",
       "      <th>3614</th>\n",
       "      <th>3615</th>\n",
       "      <th>3616</th>\n",
       "      <th>3617</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>...</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "      <td>600036.SH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>39.23</td>\n",
       "      <td>38.35</td>\n",
       "      <td>37.99</td>\n",
       "      <td>38.05</td>\n",
       "      <td>38.38</td>\n",
       "      <td>38.02</td>\n",
       "      <td>37.54</td>\n",
       "      <td>37.35</td>\n",
       "      <td>38.45</td>\n",
       "      <td>38.29</td>\n",
       "      <td>...</td>\n",
       "      <td>16.27</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.52</td>\n",
       "      <td>16.88</td>\n",
       "      <td>17.88</td>\n",
       "      <td>16.88</td>\n",
       "      <td>17.33</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.71</td>\n",
       "      <td>17.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>39.56</td>\n",
       "      <td>39.29</td>\n",
       "      <td>38.7</td>\n",
       "      <td>38.28</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.66</td>\n",
       "      <td>38.16</td>\n",
       "      <td>37.78</td>\n",
       "      <td>38.6</td>\n",
       "      <td>38.83</td>\n",
       "      <td>...</td>\n",
       "      <td>16.46</td>\n",
       "      <td>16.35</td>\n",
       "      <td>16.7</td>\n",
       "      <td>17.17</td>\n",
       "      <td>17.91</td>\n",
       "      <td>16.98</td>\n",
       "      <td>17.43</td>\n",
       "      <td>17.69</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>38.99</td>\n",
       "      <td>38.32</td>\n",
       "      <td>37.98</td>\n",
       "      <td>37.86</td>\n",
       "      <td>38.0</td>\n",
       "      <td>37.86</td>\n",
       "      <td>37.54</td>\n",
       "      <td>37.32</td>\n",
       "      <td>37.28</td>\n",
       "      <td>38.21</td>\n",
       "      <td>...</td>\n",
       "      <td>16.03</td>\n",
       "      <td>15.95</td>\n",
       "      <td>15.97</td>\n",
       "      <td>16.45</td>\n",
       "      <td>16.8</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.78</td>\n",
       "      <td>17.31</td>\n",
       "      <td>17.34</td>\n",
       "      <td>17.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>39.4</td>\n",
       "      <td>39.22</td>\n",
       "      <td>38.35</td>\n",
       "      <td>37.91</td>\n",
       "      <td>38.02</td>\n",
       "      <td>38.52</td>\n",
       "      <td>37.85</td>\n",
       "      <td>37.74</td>\n",
       "      <td>37.3</td>\n",
       "      <td>38.8</td>\n",
       "      <td>...</td>\n",
       "      <td>16.31</td>\n",
       "      <td>16.33</td>\n",
       "      <td>16.15</td>\n",
       "      <td>17.07</td>\n",
       "      <td>16.94</td>\n",
       "      <td>16.91</td>\n",
       "      <td>16.91</td>\n",
       "      <td>17.36</td>\n",
       "      <td>17.73</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>39.22</td>\n",
       "      <td>38.35</td>\n",
       "      <td>37.91</td>\n",
       "      <td>38.02</td>\n",
       "      <td>38.52</td>\n",
       "      <td>37.85</td>\n",
       "      <td>37.74</td>\n",
       "      <td>37.3</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.3</td>\n",
       "      <td>...</td>\n",
       "      <td>16.33</td>\n",
       "      <td>16.15</td>\n",
       "      <td>17.07</td>\n",
       "      <td>16.94</td>\n",
       "      <td>16.91</td>\n",
       "      <td>16.91</td>\n",
       "      <td>17.36</td>\n",
       "      <td>17.73</td>\n",
       "      <td>17.71</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>0.4589</td>\n",
       "      <td>2.2686</td>\n",
       "      <td>1.1606</td>\n",
       "      <td>-0.2893</td>\n",
       "      <td>-1.298</td>\n",
       "      <td>1.7701</td>\n",
       "      <td>0.2915</td>\n",
       "      <td>1.1796</td>\n",
       "      <td>-3.866</td>\n",
       "      <td>1.3055</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>1.11</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-2.09</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>755913.07</td>\n",
       "      <td>993428.46</td>\n",
       "      <td>902691.56</td>\n",
       "      <td>542150.38</td>\n",
       "      <td>611195.82</td>\n",
       "      <td>805855.36</td>\n",
       "      <td>603104.08</td>\n",
       "      <td>640315.89</td>\n",
       "      <td>1117982.8</td>\n",
       "      <td>586888.76</td>\n",
       "      <td>...</td>\n",
       "      <td>873021.99</td>\n",
       "      <td>1247175.72</td>\n",
       "      <td>2052232.76</td>\n",
       "      <td>1121898.32</td>\n",
       "      <td>1465692.5</td>\n",
       "      <td>803598.62</td>\n",
       "      <td>1019431.96</td>\n",
       "      <td>774277.06</td>\n",
       "      <td>914013.73</td>\n",
       "      <td>746135.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>2972981.821</td>\n",
       "      <td>3875223.368</td>\n",
       "      <td>3473126.152</td>\n",
       "      <td>2060744.582</td>\n",
       "      <td>2336249.857</td>\n",
       "      <td>3101731.956</td>\n",
       "      <td>2287237.539</td>\n",
       "      <td>2409086.954</td>\n",
       "      <td>4213094.969</td>\n",
       "      <td>2267252.621</td>\n",
       "      <td>...</td>\n",
       "      <td>1417365.938</td>\n",
       "      <td>2011436.062</td>\n",
       "      <td>3344313.047</td>\n",
       "      <td>1879278.148</td>\n",
       "      <td>2530625.181</td>\n",
       "      <td>1351767.271</td>\n",
       "      <td>1742282.28</td>\n",
       "      <td>1348702.948</td>\n",
       "      <td>1615349.974</td>\n",
       "      <td>1332370.091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3618 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0            1            2            3            4     \\\n",
       "ts_code       600036.SH    600036.SH    600036.SH    600036.SH    600036.SH   \n",
       "trade_date     20241225     20241224     20241223     20241220     20241219   \n",
       "open              39.23        38.35        37.99        38.05        38.38   \n",
       "high              39.56        39.29         38.7        38.28        38.53   \n",
       "low               38.99        38.32        37.98        37.86         38.0   \n",
       "close              39.4        39.22        38.35        37.91        38.02   \n",
       "pre_close         39.22        38.35        37.91        38.02        38.52   \n",
       "change             0.18         0.87         0.44        -0.11         -0.5   \n",
       "pct_chg          0.4589       2.2686       1.1606      -0.2893       -1.298   \n",
       "vol           755913.07    993428.46    902691.56    542150.38    611195.82   \n",
       "amount      2972981.821  3875223.368  3473126.152  2060744.582  2336249.857   \n",
       "\n",
       "                   5            6            7            8            9     \\\n",
       "ts_code       600036.SH    600036.SH    600036.SH    600036.SH    600036.SH   \n",
       "trade_date     20241218     20241217     20241216     20241213     20241212   \n",
       "open              38.02        37.54        37.35        38.45        38.29   \n",
       "high              38.66        38.16        37.78         38.6        38.83   \n",
       "low               37.86        37.54        37.32        37.28        38.21   \n",
       "close             38.52        37.85        37.74         37.3         38.8   \n",
       "pre_close         37.85        37.74         37.3         38.8         38.3   \n",
       "change             0.67         0.11         0.44         -1.5          0.5   \n",
       "pct_chg          1.7701       0.2915       1.1796       -3.866       1.3055   \n",
       "vol           805855.36    603104.08    640315.89    1117982.8    586888.76   \n",
       "amount      3101731.956  2287237.539  2409086.954  4213094.969  2267252.621   \n",
       "\n",
       "            ...         3608         3609         3610         3611  \\\n",
       "ts_code     ...    600036.SH    600036.SH    600036.SH    600036.SH   \n",
       "trade_date  ...     20100115     20100114     20100113     20100112   \n",
       "open        ...        16.27         16.2        16.52        16.88   \n",
       "high        ...        16.46        16.35         16.7        17.17   \n",
       "low         ...        16.03        15.95        15.97        16.45   \n",
       "close       ...        16.31        16.33        16.15        17.07   \n",
       "pre_close   ...        16.33        16.15        17.07        16.94   \n",
       "change      ...        -0.02         0.18        -0.92         0.13   \n",
       "pct_chg     ...        -0.12         1.11        -5.39         0.77   \n",
       "vol         ...    873021.99   1247175.72   2052232.76   1121898.32   \n",
       "amount      ...  1417365.938  2011436.062  3344313.047  1879278.148   \n",
       "\n",
       "                   3612         3613        3614         3615         3616  \\\n",
       "ts_code       600036.SH    600036.SH   600036.SH    600036.SH    600036.SH   \n",
       "trade_date     20100111     20100108    20100107     20100106     20100105   \n",
       "open              17.88        16.88       17.33        17.69        17.71   \n",
       "high              17.91        16.98       17.43        17.69         18.0   \n",
       "low                16.8        16.68       16.78        17.31        17.34   \n",
       "close             16.94        16.91       16.91        17.36        17.73   \n",
       "pre_close         16.91        16.91       17.36        17.73        17.71   \n",
       "change             0.03          0.0       -0.45        -0.37         0.02   \n",
       "pct_chg            0.18          0.0       -2.59        -2.09         0.11   \n",
       "vol           1465692.5    803598.62  1019431.96    774277.06    914013.73   \n",
       "amount      2530625.181  1351767.271  1742282.28  1348702.948  1615349.974   \n",
       "\n",
       "                   3617  \n",
       "ts_code       600036.SH  \n",
       "trade_date     20100104  \n",
       "open              17.93  \n",
       "high              18.11  \n",
       "low               17.66  \n",
       "close             17.71  \n",
       "pre_close         18.05  \n",
       "change            -0.34  \n",
       "pct_chg           -1.88  \n",
       "vol           746135.58  \n",
       "amount      1332370.091  \n",
       "\n",
       "[11 rows x 3618 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x284c6781150>]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock600036.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3588 entries, 0 to 3587\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3588 non-null   datetime64[ns]\n",
      " 1   open        3588 non-null   float64       \n",
      " 2   high        3588 non-null   float64       \n",
      " 3   low         3588 non-null   float64       \n",
      " 4   close       3588 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 140.3 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3583</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>38.38</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.02</td>\n",
       "      <td>37.886</td>\n",
       "      <td>38.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3584</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>38.05</td>\n",
       "      <td>38.28</td>\n",
       "      <td>37.86</td>\n",
       "      <td>37.91</td>\n",
       "      <td>38.008</td>\n",
       "      <td>38.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3585</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>37.99</td>\n",
       "      <td>38.70</td>\n",
       "      <td>37.98</td>\n",
       "      <td>38.35</td>\n",
       "      <td>38.130</td>\n",
       "      <td>38.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3586</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>38.35</td>\n",
       "      <td>39.29</td>\n",
       "      <td>38.32</td>\n",
       "      <td>39.22</td>\n",
       "      <td>38.404</td>\n",
       "      <td>38.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3587</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>39.23</td>\n",
       "      <td>39.56</td>\n",
       "      <td>38.99</td>\n",
       "      <td>39.40</td>\n",
       "      <td>38.580</td>\n",
       "      <td>38.311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3583 2024-12-19  38.38  38.53  38.00  38.02  37.886  38.053\n",
       "3584 2024-12-20  38.05  38.28  37.86  37.91  38.008  38.074\n",
       "3585 2024-12-23  37.99  38.70  37.98  38.35  38.130  38.139\n",
       "3586 2024-12-24  38.35  39.29  38.32  39.22  38.404  38.201\n",
       "3587 2024-12-25  39.23  39.56  38.99  39.40  38.580  38.311"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n",
    "#data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  17.93  18.11  17.66  17.71\n",
      "1    2010-01-05  17.71  18.00  17.34  17.73\n",
      "2    2010-01-06  17.69  17.69  17.31  17.36\n",
      "3    2010-01-07  17.33  17.43  16.78  16.91\n",
      "4    2010-01-08  16.88  16.98  16.68  16.91\n",
      "...         ...    ...    ...    ...    ...\n",
      "3613 2024-12-19  38.38  38.53  38.00  38.02\n",
      "3614 2024-12-20  38.05  38.28  37.86  37.91\n",
      "3615 2024-12-23  37.99  38.70  37.98  38.35\n",
      "3616 2024-12-24  38.35  39.29  38.32  39.22\n",
      "3617 2024-12-25  39.23  39.56  38.99  39.40\n",
      "\n",
      "[3618 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3618 entries, 0 to 3617\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3618 non-null   datetime64[ns]\n",
      " 1   open        3618 non-null   float64       \n",
      " 2   high        3618 non-null   float64       \n",
      " 3   low         3618 non-null   float64       \n",
      " 4   close       3618 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.5 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>17.93</td>\n",
       "      <td>18.11</td>\n",
       "      <td>17.66</td>\n",
       "      <td>17.71</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>17.71</td>\n",
       "      <td>18.00</td>\n",
       "      <td>17.34</td>\n",
       "      <td>17.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.31</td>\n",
       "      <td>17.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>17.33</td>\n",
       "      <td>17.43</td>\n",
       "      <td>16.78</td>\n",
       "      <td>16.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>16.88</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.91</td>\n",
       "      <td>17.324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>38.38</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.02</td>\n",
       "      <td>37.886</td>\n",
       "      <td>38.053</td>\n",
       "      <td>37.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>38.05</td>\n",
       "      <td>38.28</td>\n",
       "      <td>37.86</td>\n",
       "      <td>37.91</td>\n",
       "      <td>38.008</td>\n",
       "      <td>38.074</td>\n",
       "      <td>37.480333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>37.99</td>\n",
       "      <td>38.70</td>\n",
       "      <td>37.98</td>\n",
       "      <td>38.35</td>\n",
       "      <td>38.130</td>\n",
       "      <td>38.139</td>\n",
       "      <td>37.481000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3616</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>38.35</td>\n",
       "      <td>39.29</td>\n",
       "      <td>38.32</td>\n",
       "      <td>39.22</td>\n",
       "      <td>38.404</td>\n",
       "      <td>38.201</td>\n",
       "      <td>37.535333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>39.23</td>\n",
       "      <td>39.56</td>\n",
       "      <td>38.99</td>\n",
       "      <td>39.40</td>\n",
       "      <td>38.580</td>\n",
       "      <td>38.311</td>\n",
       "      <td>37.588000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3618 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-01-04  17.93  18.11  17.66  17.71     NaN     NaN        NaN\n",
       "1    2010-01-05  17.71  18.00  17.34  17.73     NaN     NaN        NaN\n",
       "2    2010-01-06  17.69  17.69  17.31  17.36     NaN     NaN        NaN\n",
       "3    2010-01-07  17.33  17.43  16.78  16.91     NaN     NaN        NaN\n",
       "4    2010-01-08  16.88  16.98  16.68  16.91  17.324     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3613 2024-12-19  38.38  38.53  38.00  38.02  37.886  38.053  37.510000\n",
       "3614 2024-12-20  38.05  38.28  37.86  37.91  38.008  38.074  37.480333\n",
       "3615 2024-12-23  37.99  38.70  37.98  38.35  38.130  38.139  37.481000\n",
       "3616 2024-12-24  38.35  39.29  38.32  39.22  38.404  38.201  37.535333\n",
       "3617 2024-12-25  39.23  39.56  38.99  39.40  38.580  38.311  37.588000\n",
       "\n",
       "[3618 rows x 8 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          39.23  39.56\n",
       "1          38.35  39.29\n",
       "2          37.99  38.70\n",
       "3          38.05  38.28\n",
       "4          38.38  38.53\n",
       "...          ...    ...\n",
       "3613       16.88  16.98\n",
       "3614       17.33  17.43\n",
       "3615       17.69  17.69\n",
       "3616       17.71  18.00\n",
       "3617       17.93  18.11\n",
       "\n",
       "[3618 rows x 2 columns]>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17.71],\n",
       "       [17.73],\n",
       "       [17.36],\n",
       "       ...,\n",
       "       [38.35],\n",
       "       [39.22],\n",
       "       [39.4 ]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>17.93</td>\n",
       "      <td>18.11</td>\n",
       "      <td>17.66</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>17.71</td>\n",
       "      <td>18.00</td>\n",
       "      <td>17.34</td>\n",
       "      <td>17.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>17.69</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.31</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>17.33</td>\n",
       "      <td>17.43</td>\n",
       "      <td>16.78</td>\n",
       "      <td>16.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>16.88</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>38.38</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>38.05</td>\n",
       "      <td>38.28</td>\n",
       "      <td>37.86</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>37.99</td>\n",
       "      <td>38.70</td>\n",
       "      <td>37.98</td>\n",
       "      <td>38.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>38.35</td>\n",
       "      <td>39.29</td>\n",
       "      <td>38.32</td>\n",
       "      <td>39.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>39.23</td>\n",
       "      <td>39.56</td>\n",
       "      <td>38.99</td>\n",
       "      <td>39.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3618 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  17.93  18.11  17.66  17.71\n",
       "14614.0  17.71  18.00  17.34  17.73\n",
       "14615.0  17.69  17.69  17.31  17.36\n",
       "14616.0  17.33  17.43  16.78  16.91\n",
       "14617.0  16.88  16.98  16.68  16.91\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  38.38  38.53  38.00  38.02\n",
       "20077.0  38.05  38.28  37.86  37.91\n",
       "20080.0  37.99  38.70  37.98  38.35\n",
       "20081.0  38.35  39.29  38.32  39.22\n",
       "20082.0  39.23  39.56  38.99  39.40\n",
       "\n",
       "[3618 rows x 4 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>17.93</td>\n",
       "      <td>18.11</td>\n",
       "      <td>17.66</td>\n",
       "      <td>17.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>17.71</td>\n",
       "      <td>18.00</td>\n",
       "      <td>17.34</td>\n",
       "      <td>17.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.69</td>\n",
       "      <td>17.31</td>\n",
       "      <td>17.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>17.33</td>\n",
       "      <td>17.43</td>\n",
       "      <td>16.78</td>\n",
       "      <td>16.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>16.88</td>\n",
       "      <td>16.98</td>\n",
       "      <td>16.68</td>\n",
       "      <td>16.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3613</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>38.38</td>\n",
       "      <td>38.53</td>\n",
       "      <td>38.00</td>\n",
       "      <td>38.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>38.05</td>\n",
       "      <td>38.28</td>\n",
       "      <td>37.86</td>\n",
       "      <td>37.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3615</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>37.99</td>\n",
       "      <td>38.70</td>\n",
       "      <td>37.98</td>\n",
       "      <td>38.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3616</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>38.35</td>\n",
       "      <td>39.29</td>\n",
       "      <td>38.32</td>\n",
       "      <td>39.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3617</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>39.23</td>\n",
       "      <td>39.56</td>\n",
       "      <td>38.99</td>\n",
       "      <td>39.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3618 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  17.93  18.11  17.66  17.71\n",
       "1     14614.0  17.71  18.00  17.34  17.73\n",
       "2     14615.0  17.69  17.69  17.31  17.36\n",
       "3     14616.0  17.33  17.43  16.78  16.91\n",
       "4     14617.0  16.88  16.98  16.68  16.91\n",
       "...       ...    ...    ...    ...    ...\n",
       "3613  20076.0  38.38  38.53  38.00  38.02\n",
       "3614  20077.0  38.05  38.28  37.86  37.91\n",
       "3615  20080.0  37.99  38.70  37.98  38.35\n",
       "3616  20081.0  38.35  39.29  38.32  39.22\n",
       "3617  20082.0  39.23  39.56  38.99  39.40\n",
       "\n",
       "[3618 rows x 5 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x284c4684110>]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\张哲凯\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_8 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_9 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0949 - val_loss: 0.1886\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0155 - val_loss: 0.0514\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0018 - val_loss: 0.0133\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 0.0097\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 5.4702e-04 - val_loss: 0.0051\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.7381e-04 - val_loss: 0.0045\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.6147e-04 - val_loss: 0.0044\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.5306e-04 - val_loss: 0.0042\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.4480e-04 - val_loss: 0.0037\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.3266e-04 - val_loss: 0.0031\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.2018e-04 - val_loss: 0.0027\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.1103e-04 - val_loss: 0.0024\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.0457e-04 - val_loss: 0.0022\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.9996e-04 - val_loss: 0.0020\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.9713e-04 - val_loss: 0.0019\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.9457e-04 - val_loss: 0.0018\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.9224e-04 - val_loss: 0.0017\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.9046e-04 - val_loss: 0.0017\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8871e-04 - val_loss: 0.0017\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8729e-04 - val_loss: 0.0016\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8597e-04 - val_loss: 0.0016\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8471e-04 - val_loss: 0.0016\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8346e-04 - val_loss: 0.0016\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8232e-04 - val_loss: 0.0016\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8130e-04 - val_loss: 0.0016\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.8019e-04 - val_loss: 0.0015\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7875e-04 - val_loss: 0.0015\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7729e-04 - val_loss: 0.0015\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7533e-04 - val_loss: 0.0015\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 3.7293e-04 - val_loss: 0.0015\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm_8 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_9 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.94927\n",
      "均方根误差: 0.97431\n",
      "平均绝对误差: 0.72365\n",
      "R2: 0.92271\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
